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        Note that additional data was saved in quantify_data when this report was generated.

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.21

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        quantify

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2024-06-04, 12:23 CEST based on data in:

        General Statistics

        Showing 12/12 rows and 9/12 columns.
        Sample NameError rateNon-primaryReads mapped% Mapped% Proper pairsTotal seqsReads mapped% AlignedM Aligned
        E04C01.lib1
        0.27%
        27.1M
        74.3M
        99.2%
        99.2%
        74.9M
        101.4M
        88.4%
        33.1M
        E04C02.lib1
        0.27%
        21.7M
        76.7M
        99.2%
        99.2%
        77.3M
        98.4M
        89.4%
        34.6M
        E04C03.lib1
        0.27%
        23.6M
        76.7M
        99.2%
        99.2%
        77.3M
        100.3M
        89.0%
        34.4M
        E04C04.lib1
        0.27%
        23.0M
        65.8M
        99.2%
        99.2%
        66.4M
        88.9M
        88.6%
        29.4M
        E04C05.lib1
        0.25%
        24.5M
        73.0M
        99.3%
        99.3%
        73.5M
        97.5M
        88.9%
        32.7M
        E04C06.lib1
        0.35%
        11.9M
        45.6M
        99.2%
        99.2%
        46.0M
        57.5M
        89.8%
        20.6M
        E04C07.lib1
        0.24%
        27.0M
        87.7M
        99.3%
        99.3%
        88.4M
        114.7M
        89.2%
        39.4M
        E04C08.lib1
        0.27%
        24.3M
        75.3M
        99.1%
        99.1%
        76.0M
        99.6M
        88.9%
        33.8M
        E04C09.lib1
        0.27%
        20.8M
        72.1M
        99.2%
        99.2%
        72.6M
        92.8M
        89.1%
        32.4M
        E04C10.lib1
        0.27%
        26.3M
        72.0M
        99.2%
        99.2%
        72.6M
        98.4M
        88.4%
        32.1M
        E04C11.lib1
        0.28%
        24.6M
        68.6M
        99.2%
        99.2%
        69.1M
        93.1M
        88.5%
        30.6M
        E04C12.lib1
        0.27%
        28.0M
        75.5M
        99.3%
        99.3%
        76.1M
        103.5M
        88.4%
        33.6M

        Samtools

        Version: 1.20

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat. All numbers in millions.

        Created with MultiQC

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

        Created with MultiQC

        STAR

        STAR is an ultrafast universal RNA-seq aligner.DOI: 10.1093/bioinformatics/bts635.

        Alignment Scores

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Samtools1.20

        MultiQC v1.21 - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.